The development of a novel agent based long term domestic energy stock model

Lee, T (2013) The development of a novel agent based long term domestic energy stock model. Doctoral thesis, University of Reading.


Download (8MB)


This research has developed a novel long term domestic energy stock model of owneroccupied
dwellings in England. Its primary purpose is to aid policy makers in
determining appropriate policy measures to achieve CO2 emissions reductions in the
housing sector.
Current modelling techniques can provide a highly disaggregated technology rich
environment, but they do not consider the behaviour required for technological
changes to the dwelling stock. Energy efficiency improvements will only occur in the
owner-occupied sector of the housing market when owners decide to carry out such
improvements. Therefore, a stock model that can simulate this decision making
process will be of more use for policy makers in predicting the impact of different
measures designed to encourage uptake of suitable technologies.
Agent based modelling has been proposed as a solution to allow the inclusion of
individual household decision making into a long term domestic stock model. The
agents in the model represent households and have a simple additive weighting
decision making algorithm based on discrete choice survey data from the Energy
Saving Trust and Element Energy. The model has then been calibrated against historic
technology diffusion data.
Sixteen scenarios have been developed and tested in the model. The initial Business
as Usual scenarios indicate that current policies are likely to fall well short of the 2050
80% emissions reduction target, although subsequent scenarios indicate that the
target is achievable. The results also indicate that care is required when setting
subsidy levels when competing technologies are available, as there is the potential to
suppress the diffusion of technologies that offer greater potential savings.
The developed model can now be used by policy makers in testing further scenarios,
and this novel approach can be applied both regionally and in other countries, subject
to the collection of suitable input data.

Item Type: Thesis (Doctoral)
Subjects: K200 Building
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Timothy Lee
Date Deposited: 09 Aug 2018 10:53
Last Modified: 09 Aug 2018 10:53

Actions (login required)

View Item View Item


In this section...